Hierarchical Network Model Excitatory-Inhibitory Tone Shapes Alternative Strategies for Different Degrees of Uncertainty in Multi-Attribute Decisions

Abstract

We investigated two-attribute, two-alternative decision-making in a hierarchical neural network with three layers: an input layer encoding choice alternative attribute values; an intermediate layer of modules processing separate attributes; and a choice layer producing the decision. Depending on intermediate layer excitatory-inhibitory (E/I) tone, the network displays three distinct regimes characterized by linear (I), convex (II) or concave (III) choice indifference curves. In regimes I and II, each option's attribute information is additively integrated. To maximize reward at low environmental uncertainty, the system should operate in regime I. At high environmental uncertainty, reward maximization is achieved in regime III, with each attribute module selecting a favored alternative, and the ultimate decision based upon comparison between outputs of attribute processing modules. We then use these principles to examine multi-attribute decisions with autism-related deficits in E/I balance, leading to predictions of different choice patterns and overall performance between autism and neurotypicals. IntroductionWe are constantly faced with decisions between alternatives defined by multiple attributes. The true value of each attribute is at times clear, and other times uncertain. For example, on Friday one might choose between main courses at a restaurant where the flavor or healthiness attributes of all the dishes are familiar.The following Wednesday might be at a restaurant with an unknown cuisine, where one is highly uncertain as to different items' flavor or healthiness. To ensure the best meal, the brain must be able to optimize choice in both environments.Systems neuroscientists have, for many years, been studying the specific circuits engaged in this kind of multi-attribute decision-making. Based on a robust set of electrophysiology and imaging findings (Xie and Padoa-Schioppa 2016; Raghuraman and Padoa-Schioppa 2014; Padoa-Schioppa and Assad 2006; O'Neill and Schultz 2018; Morrison and Salzman 2009; Conen and Padoa-Schioppa 2015; Chib et al. 2009; Pastor-Bernier, Stasiak, and Schultz 2019), many hold that all attribute signals are available in brain areas proximal to the final decision (Levy and Glimcher 2012; Padoa-Schioppa and Conen 2017). Indeed, when attribute values are clear, multi-attribute choice theoretically is simple: linearly weight and combine all attributes associated with a choice alternative, then select the one with the larger value. Though the subjective value of an attribute might be non-linearly related to the quantity offered, when the final choice is made in an environment without uncertainty, a weighted linear combination of attributes optimizes the choice between options (Nicholson and Snyder 2007).

The midcingulate cortex and temporal integration

“…Robust inhibition and long inhibitory time constants should contribute to the extension of the time window for signal summation and thus extend local temporal receptive fields. In artificial hierarchical networks, environmental uncertainty can be dynamically captured by variations of the E/I tone (Pettine et al, 2020). In humans, dynamical integration of environmental uncertainty is circumscribed to the MCC (Behrens et al, 2007).…”